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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Paradigma Akuntansi JRIIN :Jurnal Riset Informatika dan Inovasi
Nicholas
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

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Comparison of Tubercolosis Detection Using CNN Models (AlexNet and ResNet) Putra, Adya Zizwan; Amir Mahmud Husein; Nicholas; Frederico Wijaya; Aribel
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13979

Abstract

The bacterial infection caused by Mycobacterium tubercolosis, leading to tubercolosis is a prevalent contagious disease. This bacterium commonly targets the primary respiratory organs, particularly the lungs. Tuberculosis poses a significant global health challenge and necessitates early detection for effective management. In this context, to facilitate healthcare professionals in the early detection of patients, a technology capable of accurately identifying lung conditions is required. Therefore, CNN (Convolutional Neural Network) will be employed as the algorithm for detecting lung images. The research will utilize Convolutional Neural Network models, namely AlexNet and ResNet. The study aims to compare the performance of these two models in detecting TB through the analysis of chest X-ray images. The dataset comprises X-rays from both normal patients and TB patients, totaling 4.200 data points. The training process involves dividing the data into training and validation sets, with an 80% allocation for training and 20% for validation. The evaluation results indicate that the AlexNet model demonstrates higher detection accuracy, reaching 88.33% on the validation data, while ResNet achieves 83.10%. These findings suggest that the use of CNN models, especially AlexNet, can be an effective approach to enhancing early tuberculosis detection through the interpretation of chest X-ray images, with potential implications for improving global TB management and prevention efforts.
PENGARUH LEVERAGE, UKURAN PERUSAHAAN DAN PERTUMBUHAN ASET TERHADAP NILAI PERUSAHAAN Nicholas; Susanto, Liana
Jurnal Paradigma Akuntansi Vol. 7 No. 4 (2025): Oktober 2025
Publisher : Fakultas Ekonomi, Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jpa.v7i4.35013

Abstract

The objective of this research is to gather empirical data on how leverage, firm size, and asset growth impact firm value.Property and real estate firms listed on the Indonesia Stock Exchange for the research period 2019–2021 make up the study's population. This study uses purposive sampling method. The sample are 39 firms. The research data are analyzed using multiple linear regression technique which were processed by IBM SPSS 23. The results of this study show that leverage, firm size and asset growth affect firm value simultaneously. Leverage has no clear impact on firm value, however firm size and asset growth have a positive and significant impact. The study's implication is that in order to boost the firm's value and investors' interest, total assets must be increased.
Komparasi Algoritma Machine Learning Regresi untuk Prediksi Durasi Perjalanan Taksi di New York City Nicholas; Wibowo, M.Taufik; Abdillah, Ridwan; Hafizh, Raffi Hibban; Rosyani, Perani
Jurnal Riset Informatika dan Inovasi Vol 3 No 9 (2026): JRIIN : Jurnal Riset Informatika dan Inovasi (INPRESS)
Publisher : shofanah Media Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Estimasi waktu tempuh atau Estimated Time of Arrival (ETA) memiliki dampak langsung terhadap efisiensi operasional armada transportasi dan kepuasan konsumen. Namun, memprediksi durasi perjalanan di kota metropolitan seperti New York City (NYC) merupakan tantangan kompleks karena dipengaruhi oleh faktor dinamis seperti jarak, waktu, dan pola lalu lintas. Penelitian ini bertujuan untuk membangun dan membandingkan kinerja model machine learning berbasis regresi untuk memprediksi durasi perjalanan taksi NYC. Algoritma yang diuji meliputi Linear Regression, Random Forest, Gradient Boosting, dan XGBoost. Penelitian ini menerapkan tahapan preprocessing data spasial-temporal dan feature engineering yang komprehensif. Hasil eksperimen menunjukkan bahwa model berbasis ensemble dan boosting mengungguli model linear sederhana. Secara khusus, algoritma XGBoost menghasilkan kinerja terbaik dengan tingkat kesalahan prediksi terendah (RMSE dan MAE minimum) dibandingkan model lainnya serta baseline penelitian sebelumnya.
Co-Authors Abdillah, Ridwan Amir Mahmud Husein Aribel Frederico Wijaya Hafizh, Raffi Hibban Liana Susanto Perani Rosyani Putra, Adya Zizwan Wibowo, M.Taufik